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Analysis of Employment Information of University Graduates through Data Mining

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Abstract—

The employment information of university graduates contains a lot of useful information that can provide guidance for employment. This paper studied the decision tree method in data mining and improved C4.5 with Taylor’s median theorem in order to further improve its computational efficiency. The information of the 2021 graduates of China West Normal University was used as an example for analysis. It was found that practical ability, English level, and computer level had a great influence on graduate destination. In addition, compared with the traditional C4.5 algorithm, the improved C4.5 algorithm was much more efficient. The calculation time of the improved C4.5 algorithm was 6.27% shorter than the traditional C4.5 algorithm when analyzing 50 000 data. The improved C4.5 algorithm had an average accuracy of 89.81% when analyzing 200 data. The experimental results demonstrate the reliability of the improved C4.5 algorithm for employment information analysis and its applicability in practical employment management.

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REFERENCES

  1. Li, Yu., Li, N., and Jiang, M., Employment analysis of university students majoring in environmental materials based on human resources information system, J. Phys.: Conf. Ser., 2020, vol. 1549, no. 2, p. 022108. https://doi.org/10.1088/1742-6596/1549/2/022108

    Article  Google Scholar 

  2. Marozzo, F., Talia, D., and Trunfio, P., A workflow management system for scalable data mining on clouds, IEEE Trans. Services Comput., 2018, vol. 11, no. 3, pp. 480–492. https://doi.org/10.1109/tsc.2016.2589243

    Article  Google Scholar 

  3. Yu, H. and Zhang, Z., The application of data mining technology in employment analysis of university graduates, 2018 IEEE/ACIS 17th Int. Conf. on Computer and Information Science (ICIS), Singapore, 2018, IEEE, 2018, pp. 846–849. https://doi.org/10.1109/icis.2018.8466511

  4. Saravanan, P. and Rajasekaran, K., Comparative study on TAHDCO and SIDBI training programme and its impact of employment prediction based on decision trees and naive Bayes classifier, Waffen- Kostumkunde, 2020, vol. 11, no. 3, pp. 25–34. https://doi.org/10.37896/wj11.03

  5. Prabakaran, S. and Mitra, S., Survey of analysis of crime detection techniques using data mining and machine learning, J. Phys.: Conf. Ser., 2018, vol. 1000, p. 012046. https://doi.org/10.1088/1742-6596/1000/1/012046

    Article  Google Scholar 

  6. Sun, L., Yin, C., Liu, Y., Wang, H., and Ding, Y., Information to intelligence(itoI): A prototype for employment prediction of graduates based on multidimensional data, 9th Int. Conf. on Information Technology in Medicine and Education (ITME), Hangzhou, China, 2018, IEEE, 2018, pp. 834–836.

  7. Yeung, Ch.-K. and Yeung, D.-Ya., Incorporating features learned by an enhanced deep knowledge tracing model for STEM/non-STEM job prediction, Int. J. Artif. Intell. Educ., 2019, vol. 29, no. 3, pp. 317–341. https://doi.org/10.1007/s40593-019-00175-1

    Article  Google Scholar 

  8. Guo, T., Xia, F., Zhen, S., Bai, X., Zhang, D., Liu, Z., and Tang, J., Graduate employment prediction with bias, Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 1, pp. 670–677. https://doi.org/10.1609/aaai.v34i01.5408

  9. Lin, X., College student employment data platform based on FPGA and machine learning, Microprocessors Microsyst., 2020, p. 103471. https://doi.org/10.1016/j.micpro.2020.103471

    Book  Google Scholar 

  10. Nahar, N. and Ara, F., Liver disease prediction by using different decision tree techniques, Int. J. Data Min. Knowl. Manage. Process, 2018, vol. 8, no. 2, pp. 1–9. https://doi.org/10.5121/ijdkp.2018.8201

    Article  Google Scholar 

  11. Lee, C.K., Hofer, I., Gabel, E., Baldi, P., and Cannesson, M., Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality, Anesthesiology, 2018, vol. 129, no. 4, pp. 649–662. https://doi.org/10.1097/aln.0000000000002186

    Article  PubMed  Google Scholar 

  12. Dawid, H. and Kopel, M., On economic applications of the genetic algorithm: A model of the cobweb type, J. Evol. Econ., 2019, vol. 8, no. 3, pp. 297–315. https://doi.org/10.1007/s001910050066

    Article  Google Scholar 

  13. Roy, P.T., Ricci, S., Dupuis, R., Campet, R., Jouhaud, J., and Fournier, C., BATMAN: Statistical analysis for expensive computer codes made easy, J. Open Source Software, 2018, vol. 3, no. 21, p. 493. https://doi.org/10.21105/joss.00493

    Article  ADS  Google Scholar 

  14. Buaton, R., Mawengkang, H., Zarlis, M., Effendi, S., Hara Pardede, A.M., Maulita, Ya., Fauzi, A., Novriyenni, N., Sihombing, A., and Lumbanbatu, K., Decision tree optimization in data mining with support and confidence, J. Phys.: Conf. Ser., 2019, vol. 1255, no. 1, p. 012056. https://doi.org/10.1088/1742-6596/1255/1/012056

    Article  Google Scholar 

  15. Mohanty, M., Sahoo, S., Biswal, P., and Sabut, S., Efficient classification of ventricular arrhythmias using feature selection and C4.5 classifier, Biomed. Signal Process. Control, 2018, vol. 44, pp. 200–208. https://doi.org/10.1016/j.bspc.2018.04.005

    Article  Google Scholar 

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This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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  1. Center for Innovation and Entrepreneurship, China West Normal University, 637000, Nanchong, Sichuan, China

    Lihui Hu

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  1. Lihui Hu
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Correspondence to Lihui Hu.

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Lihui Hu Analysis of Employment Information of University Graduates through Data Mining. Aut. Control Comp. Sci. 58, 58–65 (2024). https://doi.org/10.3103/S0146411624010073

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